Offline human-assisted surface quality inspection techniques are time-consuming and need labor intensive setup requirements that are sometimes unacceptable due to their slowing down of manufacturing productivity. This paper proposes an efficient surface roughness inspection method which uses photometric stereo and probabilistic neural network (PNN). A photometric stereo scheme with six images was used to generate a 3D depth map of the machined surface. Probabilistic neural network and statistical features extracted from the depth map were employed for the classification of surface roughness quality. The method was validated on several machined surfaces with different surface roughnesses. The satisfying accuracy of the introduced method demonstrates the feasibility of the proposed system.